mirror of
https://github.com/lordmathis/CUDANet.git
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292 lines
9.6 KiB
Plaintext
292 lines
9.6 KiB
Plaintext
#include <cuda_runtime_api.h>
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#include <gtest/gtest.h>
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#include <iostream>
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#include "conv2d.cuh"
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class Conv2dTest : public ::testing::Test {
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protected:
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CUDANet::Layers::Conv2d commonTestSetup(
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int inputSize,
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int inputChannels,
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int kernelSize,
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int stride,
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int numFilters,
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CUDANet::Layers::Padding padding,
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CUDANet::Layers::ActivationType activationType,
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std::vector<float>& input,
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float* kernels,
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float*& d_input
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) {
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// Create Conv2d layer
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CUDANet::Layers::Conv2d conv2d(
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inputSize, inputChannels, kernelSize, stride, numFilters, padding,
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activationType
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);
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conv2d.setWeights(kernels);
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// Allocate device memory
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cudaStatus = cudaMalloc(
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(void**)&d_input,
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sizeof(float) * inputSize * inputSize * inputChannels
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);
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EXPECT_EQ(cudaStatus, cudaSuccess);
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// // Copy input to device
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cudaStatus = cudaMemcpy(
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d_input, input.data(), sizeof(float) * input.size(),
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cudaMemcpyHostToDevice
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);
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EXPECT_EQ(cudaStatus, cudaSuccess);
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return conv2d;
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}
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void commonTestTeardown(float* d_input) {
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// Free device memory
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cudaFree(d_input);
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}
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cudaError_t cudaStatus;
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};
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TEST_F(Conv2dTest, SimpleTest) {
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int inputSize = 4;
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int inputChannels = 1;
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int kernelSize = 2;
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int stride = 1;
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int numFilters = 1;
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CUDANet::Layers::Padding padding = CUDANet::Layers::Padding::VALID;
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CUDANet::Layers::ActivationType activationType =
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CUDANet::Layers::ActivationType::NONE;
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std::vector<float> input = {1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f,
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7.0f, 8.0f, 9.0f, 10.0f, 11.0f, 12.0f,
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13.0f, 14.0f, 15.0f, 16.0f};
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std::vector<float> kernels = {
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1.0f,
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2.0f,
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3.0f,
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4.0f,
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};
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float* d_input;
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float* d_output;
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CUDANet::Layers::Conv2d conv2d = commonTestSetup(
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inputSize, inputChannels, kernelSize, stride, numFilters, padding,
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activationType, input, kernels.data(), d_input
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);
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int outputSize = (inputSize - kernelSize) / stride + 1;
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EXPECT_EQ(outputSize, conv2d.getOutputSize());
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d_output = conv2d.forward(d_input);
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std::vector<float> expected = {44.0f, 54.0f, 64.0f, 84.0f, 94.0f,
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104.0f, 124.0f, 134.0f, 144.0f};
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std::vector<float> output(outputSize * outputSize * numFilters);
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cudaStatus = cudaMemcpy(
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output.data(), d_output, sizeof(float) * output.size(),
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cudaMemcpyDeviceToHost
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);
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EXPECT_EQ(cudaStatus, cudaSuccess);
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for (int i = 0; i < output.size(); ++i) {
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EXPECT_FLOAT_EQ(expected[i], output[i]);
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}
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commonTestTeardown(d_input);
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}
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TEST_F(Conv2dTest, PaddedTest) {
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int inputSize = 5;
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int inputChannels = 3;
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int kernelSize = 3;
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int stride = 1;
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int numFilters = 2;
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CUDANet::Layers::Padding padding = CUDANet::Layers::Padding::SAME;
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CUDANet::Layers::ActivationType activationType =
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CUDANet::Layers::ActivationType::NONE;
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// clang-format off
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std::vector<float> input = {
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// Channel 1
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0.823f, 0.217f, 0.435f, 0.981f, 0.742f,
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0.109f, 0.518f, 0.374f, 0.681f, 0.147f,
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0.956f, 0.729f, 0.654f, 0.087f, 0.392f,
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0.784f, 0.921f, 0.543f, 0.231f, 0.816f,
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0.472f, 0.614f, 0.102f, 0.987f, 0.398f,
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// Channel 2
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0.051f, 0.756f, 0.841f, 0.293f, 0.128f,
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0.417f, 0.632f, 0.095f, 0.184f, 0.529f,
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0.871f, 0.958f, 0.213f, 0.347f, 0.725f,
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0.461f, 0.012f, 0.278f, 0.195f, 0.649f,
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0.853f, 0.707f, 0.988f, 0.988f, 0.322f,
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// Channel 3
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0.345f, 0.123f, 0.789f, 0.123f, 0.456f,
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0.456f, 0.789f, 0.123f, 0.345f, 0.123f,
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0.789f, 0.123f, 0.345f, 0.123f, 0.456f,
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0.123f, 0.345f, 0.123f, 0.789f, 0.123f,
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0.345f, 0.123f, 0.789f, 0.123f, 0.456f
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};
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std::vector<float> kernels = {
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// Filter 1, Channel 1
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0.128f, 0.754f, 0.987f,
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0.321f, 0.412f, 0.635f,
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0.298f, 0.017f, 0.845f,
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// Filter 1, Channel 2
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0.514f, 0.729f, 0.952f,
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0.684f, 0.378f, 0.159f,
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0.823f, 0.547f, 0.216f,
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// Filter 1, Channel 3
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0.983f, 0.231f, 0.456f,
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0.178f, 0.654f, 0.821f,
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0.345f, 0.987f, 0.123f,
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// Filter 2, Channel 1
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0.789f, 0.543f, 0.210f,
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0.012f, 0.371f, 0.638f,
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0.456f, 0.198f, 0.907f,
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// Filter 2, Channel 2
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0.101f, 0.432f, 0.759f,
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0.234f, 0.567f, 0.890f,
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0.543f, 0.876f, 0.219f,
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// Filter 2, Channel 3
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0.345f, 0.678f, 0.011f,
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0.678f, 0.011f, 0.345f,
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0.011f, 0.345f, 0.678f
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};
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// clang-format on
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float* d_input;
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float* d_output;
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CUDANet::Layers::Conv2d conv2d = commonTestSetup(
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inputSize, inputChannels, kernelSize, stride, numFilters, padding,
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activationType, input, kernels.data(), d_input
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);
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EXPECT_EQ(inputSize, conv2d.getOutputSize());
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d_output = conv2d.forward(d_input);
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std::vector<float> output(
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conv2d.getOutputSize() * conv2d.getOutputSize() * numFilters
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);
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cudaMemcpy(
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output.data(), d_output,
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sizeof(float) * conv2d.getOutputSize() * conv2d.getOutputSize() *
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numFilters,
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cudaMemcpyDeviceToHost
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);
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// Generated by tools/generate_conv2d_test.py
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std::vector<float> expected = {
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// Channel 1
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2.29426f, 3.89173f, 4.17634f, 3.25501f, 2.07618f, 5.41483f, 7.09971f,
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6.39811f, 5.71432f, 3.10928f, 5.12973f, 6.29638f, 5.26962f, 5.21997f,
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3.05852f, 6.17517f, 7.19311f, 6.69771f, 6.2142f, 4.03242f, 3.3792f,
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4.36444f, 4.396f, 4.69905f, 3.62061f,
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// Channel 2
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2.87914f, 3.71743f, 3.51854f, 2.98413f, 1.46579f, 4.94951f, 6.18983f,
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4.98187f, 4.38372f, 3.35386f, 5.0364f, 5.3756f, 4.05993f, 4.89299f,
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2.78625f, 5.33763f, 5.80899f, 5.89785f, 5.51095f, 3.74287f, 2.64053f,
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4.05895f, 3.96482f, 4.30177f, 1.94269f
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};
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for (int i = 0; i < output.size(); i++) {
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EXPECT_NEAR(output[i], expected[i], 0.0001f);
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}
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commonTestTeardown(d_input);
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}
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TEST_F(Conv2dTest, StridedPaddedConvolution) {
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int inputSize = 5;
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int inputChannels = 2;
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int kernelSize = 3;
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int stride = 2;
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int numFilters = 2;
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CUDANet::Layers::Padding padding = CUDANet::Layers::Padding::SAME;
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CUDANet::Layers::ActivationType activationType =
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CUDANet::Layers::ActivationType::RELU;
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// clang-format off
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std::vector<float> input = {
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// Channel 1
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0.946f, 0.879f, 0.382f, 0.542f, 0.453f,
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0.128f, 0.860f, 0.778f, 0.049f, 0.974f,
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0.400f, 0.874f, 0.161f, 0.271f, 0.580f,
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0.373f, 0.078f, 0.366f, 0.396f, 0.181f,
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0.246f, 0.112f, 0.179f, 0.979f, 0.026f,
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// Channel 2
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0.598f, 0.458f, 0.776f, 0.213f, 0.199f,
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0.853f, 0.170f, 0.609f, 0.269f, 0.777f,
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0.776f, 0.694f, 0.430f, 0.238f, 0.968f,
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0.473f, 0.303f, 0.084f, 0.785f, 0.444f,
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0.464f, 0.413f, 0.779f, 0.298f, 0.783f
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};
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std::vector<float> kernels = {
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// Filter 1, Channel 1
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0.744f, 0.745f, 0.641f,
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0.164f, 0.157f, 0.127f,
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0.732f, 0.761f, 0.601f,
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// Filter 1, Channel 2
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0.475f, 0.335f, 0.499f,
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0.833f, 0.793f, 0.176f,
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0.822f, 0.163f, 0.175f,
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// Filter 2, Channel 1
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0.918f, 0.340f, 0.497f,
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0.233f, 0.218f, 0.847f,
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0.931f, 0.926f, 0.199f,
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// Filter 2, Channel 2
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0.510f, 0.432f, 0.567f,
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0.236f, 0.397f, 0.739f,
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0.939f, 0.891f, 0.006f
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};
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// clang-format on
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float* d_input;
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float* d_output;
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CUDANet::Layers::Conv2d conv2d = commonTestSetup(
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inputSize, inputChannels, kernelSize, stride, numFilters, padding,
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activationType, input, kernels.data(), d_input
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);
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EXPECT_EQ(inputSize, conv2d.getOutputSize());
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d_output = conv2d.forward(d_input);
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std::vector<float> output(
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conv2d.getOutputSize() * conv2d.getOutputSize() * numFilters
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);
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cudaMemcpy(
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output.data(), d_output,
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sizeof(float) * conv2d.getOutputSize() * conv2d.getOutputSize() *
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numFilters,
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cudaMemcpyDeviceToHost
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);
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// Generated by tools/generate_conv2d_test.py
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std::vector<float> expected = {
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// Channel 1
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0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 1.59803f, 2.84444f, 1.6201f, 0.0f,
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0.0f, 2.38937f, 3.80762f, 3.39679f, 0.0f, 0.0f, 1.13102f, 2.33335f,
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1.98488f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f,
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// Channel 2
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0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 2.57732f, 3.55543f, 2.24675f, 0.0f,
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0.0f, 3.36842f, 3.41373f, 3.14804f, 0.0f, 0.0f, 1.17963f, 2.55005f,
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1.63218f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f
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};
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for (int i = 0; i < output.size(); i++) {
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EXPECT_NEAR(output[i], expected[i], 0.0001f);
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}
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commonTestTeardown(d_input);
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}
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